Monitoring and controlling air quality parameters form an important subject of atmospheric and environmental research today due to the health impacts caused by the different pollutants present in the urban areas. The support vector machine (SVM), as a supervised learning analysis method, is considered an effective statistical tool for the prediction and analysis of air quality. The work presented here examines the feasibility of applying the SVM to predict the ozone and particle concentrations in two Tunisian cities, namely Tunis and Sfax. We used the SVM with the linear kernel, SVM with the polynomial kernel and SVM with the RBF kernel to predict the ozone and particle concentrations in Tunisia for one year. The RBF kernel produced good results for the two pollutants with 0% error rate. Polynomial and linear kernels produced sufficiently low errors for the pollutants, at 9.09% and 18.18%, respectively. Discriminant Analysis (DA) was selected to analyze the datasets of two air quality parameters, namely ozone O3 and Suspended Particles SP. The DA results show that the spatial characterization allows for the successful discrimination between the two cities with an error rate of 4.35% in the case of the linear DA and 0% in the case of the quadratic DA. A thematic map of Tunisia was created using the MapInfo software.

This study was carried out for the purpose of investigating the issue of tailings dams as a potential source of contamination by trace elements in soils at the Sidi Bou Othmane mine in the Marrakech Region, Morocco. Soil samples taken from depths of up to 15 cm and within a radius of 50 m from the tailings dams, were analyzed for Cd, Cu, Pb and Zn using atomic absorption spectrometry. Average concentrations of Cd, Cu, Pb and Zn in soils around the abandoned tailings dams were, respectively, 157.2±8.8, 969.1±38.7, 1640.7±42.7 and 2846.8±84.6 mg/kg. Soils in the vicinity of the decommissioned tailings dams registered increased values for Cd, Cu, Pb and Zn. Contamination factors (CF) and pollution index (PI) were calculated in order to estimate the anthropogenic contribution of target pollutants which determined Cd, Cu, Pb and Zn as the main pollutants in this region. The results revealed the polluted areas in the vicinity of the mine, especially two rural communities apparently linked to the lack of appropriate measures to counteract the effects of these mine tailings, which were causing a progressive contamination of the soil with residues of heavy metal emissions in this region under study. Improved strategies for the management of tailings, among other factors, might have influenced the reduced level of contamination by trace elements at the abandoned mine tailings dam sites.

Groundwater aquifers are an important source of water supply for agriculture, industry and drinking. The present study investigates the changes in the quality of groundwater using geostatistical methods in the Ravar plain during a 10-year period. In this study, after selecting the appropriate spatial interpolation method to draw water quality parameters such as TDS, SAR and EC, zoning maps of Ravar plain were provided for three periods of time: the first period (2002-2005), an intermediate period (2006-2009) and the final period (2010-2012) in two seasons using ArcGIS 10.1. For this purpose, data were evaluated in GS+ 5.1 software, after calculation, the best model with the lowest estimated error was selected for zoning water quality parameters. Because of the lowest estimation error, Kriging, Gaussian and Spherical variogram models were selected as appropriate interpolation method for zoning the quality parameters. The results of the spatial analysis of TDS showed that TDS have been increased in the study area. Due to the amount of dissolved solids, EC amount was highly variable. According to the Wilcox classification, at the end of the period, water quality of agricultural was inappropriate in most of the area which represents the increase of electrical conductivity during the period.

Identifying Municipal Solid Waste (MSW) disposal sites and appropriately managing them is a challenging task to many developing countries like Bangladesh. It is a complex issue in an urban area, as increasing population levels, rapid economic growth and rise in community living standard, accelerates the generation rate of MSW. The study area is zone-2 (Mirpur-Pallabi) of Dhaka North City Corporation (DNCC) (10.40 km2) is a residential area, from where about 353.34 ton/day solid waste is generated and among them about 57.43% were managed by DNCC. There are 41 different size containers present at 17 locations in study area. The existing site and waste collecting containers are not sufficient, which deteriorates the environment due to illegal waste dumping and about 15 illegal dumping sites were identified. To identify proper waste dumping site and prevent contamination, Geographical Information System (GIS) was used to propose an efficient scenario with relocating the existing waste collecting containers and another scenario was proposed with number of containers (73) to attain an 93.68% waste collection efficiency including optimization and selection of waste collecting routes for the study area. This study also indicate that the application of GIS is an efficient and low cost tool to study and select appropriate dumping site so as to facilitate decision making processes.

Bioremoval and bioreduction activities of hydrocarbon (diesel) isolation from environmental samples were studied by the activity of biosurfactant production, and calculating emulsification index, gravimetric, and FTIR analysis along with the estimation of bacterial biomass. Sample from soil near petrol, diesel pumps and water sample from Thesjaswini River near Padannakad, Kasaragod, Kerala, India, were used to screen the potential diesel oil utilizing bacteria. Among the bacterial isolates (Staphylococcus, Bacillus and Corynebacterium strains), Staphylococcus sp was the potent degraders of diesel oil. Staphylococcus strain was observed to be maximum diesel oil utilizing ability (73% emulsification index) and change in the functional groups of the compound (FTIR analysis). The strain showed optimal growth at 37oC with pH 7, agitation of 150 rpm and time period (5days). The results revealed the possibility to use these strain for the reduction of complex hydrocarbon in ecosystems where they accumulate and cause pollution problems. The highest rate of hydrocarbon degradation occurred when the bacterial strain is a biosurfactants producer. The selective strain produces biosurfactants which increase the interfacial area for contact to give improved uptake of hydrophobic substrates. Bacterial strains capable of degrading complex hydrocarbons, present in the environment, have a potential to be used as an effective tool for removing ecotoxic compounds. Furthermore, results indicated that the bacterial strain Staphylococcus sp could be potentially used in biodegradation of diesel oil in waste water and had a promising application in bioremediation of hydrocarbon contaminated environments.

Evaluation of surface water quality is a complex process undertakingmultiple parameters. Converting great amount of parameters into a simpler expressionand enabling easy interpretation of data are the main purposes of water quality indices.The main aim of this study is to plan effective water resources management system forKarun River by combination of CCMEWQI and Geographic Information System (GIS).The investigation was carried out to set a management plan through exploratory andspatial analysis of physicochemical water parameters of collected samples from 10stations over one year period. Since all indices were obtained from index, river zoningwas conducted by GIS. Moreover, trace metals concentrations (As, Cr, Cd, Fe, Zn, Mn,and Al) ranged in safer limit. The highest values of F1 belonged to aquatic life and thelowest ones belonged to irrigation. Aquatic life and drinking uses received the maximumvalues of F2. The lowest values were devoted to livestock and then recreation uses. It wasinferred from index that the quality of the Karun River is principally impacted by highturbidity, TDS, NO3, SO4, and PO4 due to high suspended sediment loads. The maincause is incremental agricultural, industrial, and residential effluents. Amongst stations,station one only received the priority for drinking water supply and recreation.

The estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. In this paper, we investigate the spatial distribution of three air pollutants in Tehran’s atmosphere: carbon monoxide (CO), nitrogen dioxide (NO2), and atmospheric particulate matters less than 10 μm in diameter (PM10μm). To do this, we use four geostatistical interpolation methods: Ordinary Kriging, Universal Kriging, Simple Kriging, and Ordinary Cokriging with Gaussian semivariogram, to estimate the spatial distribution surface for three mentioned air pollutants in Tehran’s atmosphere. The data were collected from 21 air quality monitoring stations located in different districts of Tehran during 2012 and 2013 for 00UTC. Finally, we evaluate the Kriging estimated surfaces using three statistical validation indexes: mean absolute error (MAE), root mean square error (RMSE) that can be divided into systematic and unsystematic errors (RMSES, RMSEU), and D-Willmot. Estimated standard errors surface or uncertainty band of each estimated pollutant surface was also developed. The results indicated that using two auxiliary variables that have significant correlation with CO, the ordinary Cokriginga scheme for CO consistently outperforms all interpolation methods for estimating this pollutant and simple Kriging is the best model for estimation of NO2 and PM10. According to optimal model, the highest concentrations of PM10 are observed in the marginal areas of Tehran while the highest concentrations of NO2 and CO are observed in the central and northern district of Tehran.

The idea of systems analysis and mathematical modeling for formulating and resolving river pollution issues is of relatively recent vintage and has been applied widely in the last 3 decades. The present study illustrates the utility of Beck-modified Khanna–Bhutiani model (BMKB) to determine the pollution load due to the presence of organic matter in River Ganga from its course from Devprayag to Roorkee through the holy city of India, Haridwar. The study was conducted over a period of 3 years between 2010 and 2013. The study was aimed to verify the BMKB model for River Ganga. This model was simulated and calibrated through the data obtained by model by comparing it with the field data observed manually. Paired T-test were performed for dissolved oxygen (DO) and biochemical oxygen demand (BOD) between the titrated value and modelled value to determine if there was any statistically significant difference between the means of respective values. The results of T-test revealed statistically significant difference between DO and BOD, i.e., DO t (11)= 3.819, P= 0.003, BOD t(11)= 14.635, P= 0.000. The model presented with a good agreement between the calibrated and observed data, thereby actualizing the validity of the proposed model.

A four year long research programme divided into four phases has been designed for the first time to assess the combined Noise-Air quality and combined exposure to Noise-Air pollution at the curbside open-air microenvironment of Kolkata city, India. The results and the findings of the different phases of study had already been published. The objective of the review work is to fuse the findings of the different phases of studies; to identify the factor(s) influences the degree of correlation between concentration of the air pollutant(s) and traffic noise level at the microenvironment of the city. Incidence of moderate to very strong positive Pearson’s correlation coefficients between concentration of the air pollutant(s) and traffic noise level indicated chances of correlated exposure to these environmental stressors at the microenvironment of the city. Average combined Noise-Air quality and combined exposure to Noise-Air pollution of the microenvironment of the city was very poor (0.17) to fair (0.50) and poor (-0.06) to excellent (3.33) respectively. The best combined Noise-Air quality and the best level of combined exposure to Noise-Air pollution at the microenvironment of the city was prominently evidenced in after-noon, in summer and under variable road geometry. However, the worst combined Noise-Air quality and the worst level of combined exposure to Noise-Air pollution at the microenvironment of the city was prominently evidenced in evening, in winter and under constant road geometry. A principal component analysis revealed that intensity of wind determines the degree of correlation between the environmental stressors at the microenvironment.

This study assessed the chemical and microbial contents in the water of public swimming pools in Akwa Ibom State, Nigeria. A total of 16 public pools were selected through probability sampling from the 32 pools that existed in the five urban areas of Akwa Ibom State. Water samples were collected from the sampled pools and tested in the laboratory for chemical and microbial contents. Results showed that the chemical contents of most pools in the study area were less than the values recommended for effective disinfection of the pools. Average free chlorine ranged from 0.05 mg/L to 1.15 mg/L 200 Cfu/ml permissible, except for pools L and M, with 1.8×102 Cfu/ml and 1.7×102 Cfu/ml, respectively. Mean fecal coliform ranged from 2.0×103 Cfu/ml to 4.4×103 Cfu/ml >10 Cfu/100 ml permissible, except for pools L and M, with 0.0 Cfu/ml. It could therefore be concluded that only one out of every eight public swimming pools in Akwa Ibom State meet the minimum requirement for both chemical and microbial contents. This simply implies that most public swimming pools in the study area are not safe for users.